Knowledge catalogue
arXiv:2506.01882v3 Announce Type: replace-cross Abstract: The characterization of Hamiltonians and other components of open quantum dynamical systems plays a crucial role in quantum computing and othe
arXiv:2606.21718v1 Announce Type: cross Abstract: Detecting online polarization remains a critical challenge, particularly in multilingual and multicultural contexts where intergroup hostility is prev
arXiv:2606.22772v1 Announce Type: new Abstract: Lip-syncing deepfakes are among the most challenging forms of manipulated media because their artifacts are localized almost exclusively to the mouth re
arXiv:2412.20034v2 Announce Type: replace Abstract: Continual test-time domain adaptation (CTTA) aims to adjust pre-trained source models to perform well over time across non-stationary target environ
arXiv:2606.21119v1 Announce Type: new Abstract: Mammography is an essential tool for breast cancer detection, with millions of examinations conducted annually. However, publicly available high-quality
arXiv:2606.21329v1 Announce Type: new Abstract: Medical time series (MedTS) signals such as electroencephalography (EEG) and electrocardiography (ECG) support many clinical applications. However, subs
arXiv:2606.22400v1 Announce Type: new Abstract: This study introduces a computationally efficient convolutional neural network (CNN) architecture enhanced with transfer learning for multi-cancer detec
arXiv:2411.19700v5 Announce Type: replace Abstract: Explainable AI methods for vision models aim to identify the parts of the input that are important for the final prediction and subsequently relate
arXiv:2606.23637v1 Announce Type: new Abstract: Matrix-aware optimizers such as Muon and Muown have recently shown strong empirical performance for pre-training Transformers. In particular, Muown sepa
arXiv:2606.20682v1 Announce Type: new Abstract: Automated cheque processing requires localising key fields (date, legal amount, IFSC code, account number, signature, and payee name) before any recogni
arXiv:2606.23226v1 Announce Type: new Abstract: Remote Photoplethysmography (rPPG) enables contactless pulse estimation from facial videos, serving as a vital tool for health monitoring. However, curr
arXiv:2606.22143v1 Announce Type: cross Abstract: Planning contact-rich whole-arm manipulation is challenging because interactions that involve extended robot geometry give rise to complex contact dyn
arXiv:2510.04646v2 Announce Type: replace Abstract: Flow matching models generate high-fidelity molecular geometries but incur significant computational costs during inference, requiring hundreds of n
arXiv:2606.20512v2 Announce Type: replace-cross Abstract: LLM-based coding agents need higher-level operational knowledge about a repository (which files house which subsystems, how to run the test su
arXiv:2606.22914v1 Announce Type: new Abstract: Activities in numerous evolving systems can be represented as dynamic graphs in snapshot form at different time intervals, i.e., discrete-time dynamic g
arXiv:2606.20736v1 Announce Type: new Abstract: Static visual question answering (VQA) benchmarks age quickly: Once the items leak into training corpora, scores can reflect memorization rather than ge
arXiv:2606.20638v1 Announce Type: cross Abstract: Large language models are increasingly deployed as long-lived agents that must adapt across users, tasks, domains, modalities, and feedback regimes wi
arXiv:2606.19641v2 Announce Type: replace-cross Abstract: End-to-end autonomous driving models are typically trained on offline human-demonstration datasets that provide limited state coverage and oft
arXiv:2505.17295v2 Announce Type: replace Abstract: We introduce ScanBot, a benchmark for instruction-conditioned, high-precision surface scanning with robot-mounted industrial laser profilers. Unlike
arXiv:2606.22700v1 Announce Type: cross Abstract: Federated Learning (FL) enables collaborative model training without sharing raw data, making it a promising paradigm for privacy-sensitive applicatio
arXiv:2606.22350v1 Announce Type: new Abstract: Reinforcement Learning (RL) has been widely applied to sequential decision-making, yet it often suffers from poor sample efficiency due to costly intera
arXiv:2606.23679v1 Announce Type: new Abstract: Modern text-to-image models excel in visual fidelity and prompt adherence. However, this strict adherence comes at the cost of diversity: generated samp
arXiv:2606.20889v1 Announce Type: new Abstract: Estimating causal effects in industrial time series requires handling temporal dynamics, time-varying treatments, and unobserved confounders. Existing c
arXiv:2603.01250v3 Announce Type: replace Abstract: Breast cancer is the most frequently diagnosed malignancy among women worldwide and a leading cause of cancer-related mortality. Dynamic contrast-en
arXiv:2606.21008v1 Announce Type: cross Abstract: The metanym game is a competitive word game for LLMs that measures structural intelligence against established cognitive-science constructs. No conten
Sakana AI announced a new service called 'Fugu' that bundles multiple AI models together through programming to achieve higher performance capabilities. The approach combines various AI systems in a c
The b9761 release of llama.cpp includes server improvements with model downloading moved to a dedicated process and real-time model load progress tracking via /models/sse endpoint. The release feature
OMG! Fugu Ultra is ridiculously good at these 3D renders. Media Introducing Sakana Fugu: A full multi-agent orchestration system accessible via a single model API. Our ‘Fugu Ultra’ model matches the p
GLM 5.2, likely Alibaba's language model, has achieved significant provider adoption with approximately 20 different providers offering access to the model. This suggests widespread integration and av
We’re also launching the OpenAI Daybreak Cyber Partner Program with leading security software and services providers. Participating partners can use GPT‑5.5 with Trusted Access for Cyber in the securi
We’re expanding OpenAI Daybreak to help democratize patching vulnerable software at machine speed: - Codex Security plugin: find, validate, and fix vulnerabilities right inside Codex - The full versio
GLM 5.2 is a language model released or discussed on X (formerly Twitter) via Ollama, a platform for running large language models locally. The post expresses positive sentiment about GLM 5.2's capabi
GLM 5.2 is a language model available through Ollama that has received positive user feedback for its performance capabilities. The post appears to be a social media testimonial praising the model's q
arXiv:2505.13196v3 Announce Type: replace-cross Abstract: We introduce Velocity-Regularized Adam (VRAdam), a physics-inspired optimizer for training deep neural networks that draws on ideas from quart
arXiv:2606.11361v1 Announce Type: cross Abstract: Structured abstracts are important for biomedical literature processing, by facilitating information retrieval, text mining, and knowledge synthesis.
arXiv:2606.11456v1 Announce Type: cross Abstract: The deployment of LLM-based agents in scientific analysis raises opposing concerns: that agents may reduce methodological diversity, or that they may
arXiv:2606.12226v1 Announce Type: new Abstract: While deep learning has significantly advanced image reconstruction of Electrical Capacitance Tomography (ECT), most data-driven methods map directly be
arXiv:2606.12366v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models that couple pretrained Vision-Language Models (VLMs) with continuous action experts have achieved strong manipulatio
arXiv:2606.12346v1 Announce Type: cross Abstract: Hematoxylin and eosin (H&E) staining is the cornerstone of histopathology, yet scalable, quantitative analysis of H&E whole-slide images (WSIs) remain
arXiv:2606.11583v1 Announce Type: new Abstract: Text-attributed graphs (TAGs) underlie real-world applications such as citation networks, social media, and e-commerce. Few-shot graph learning on TAGs
arXiv:2606.11464v1 Announce Type: new Abstract: Robotic table tennis is a representative benchmark for high-speed, closed-loop robotic control in dynamic environments, where accurate and fast predicti
arXiv:2606.11337v1 Announce Type: new Abstract: Scientific AI agents increasingly retrieve evidence, reason across sources, and synthesize conclusions used in consequential decisions. Yet, their abili
arXiv:2606.12210v1 Announce Type: new Abstract: Can financial news reliably predict short-term stock movements? Despite advances in large language models, this question remains unresolved. We revisit
arXiv:2606.12075v1 Announce Type: cross Abstract: Network Intrusion Detection Systems (NIDS) heavily utlize Machine Learning (ML) but ML models can be manipulated via adversarial attacks. These attack
arXiv:2510.03520v2 Announce Type: replace-cross Abstract: Ensuring safety is a foundational requirement for large language models (LLMs). Achieving an appropriate balance between enhancing the utility
arXiv:2606.11420v1 Announce Type: new Abstract: Every day, millions absorb claims from podcasts and streams that no fact-checker ever sees. Spoken misinformation is built through conversation, where c
arXiv:2606.12088v1 Announce Type: new Abstract: Most fairness research in NLP assumes direct access to protected attributes such as gender, race, or nationality. In practice, however, such information
arXiv:2606.11616v1 Announce Type: new Abstract: High-quality training data is essential for the success of machine learning models. However, real-world datasets often contain mixed types of errors ari
arXiv:2606.12402v1 Announce Type: cross Abstract: Vision-Language Models (VLMs) are increasingly deployed as high-level planners for embodied agents, with an emerging strategy of scaling test-time com
arXiv:2601.23278v2 Announce Type: replace-cross Abstract: Diffusion Large Language Models (DLLMs) offer a compelling alternative to Auto-Regressive models, but their deployment is constrained by high
arXiv:2606.11898v1 Announce Type: new Abstract: Research on Text-Attributed Graphs (TAGs) has gained significant attention recently due to its broad applications across various real-world data scenari
arXiv:2602.19502v2 Announce Type: replace Abstract: Agentic AI systems are increasingly capable of autonomous data science workflows, yet clinical prediction tasks demand domain expertise that purely
arXiv:2606.11737v1 Announce Type: cross Abstract: Close-in exoplanets exhibit a wide range of orbital architectures and physical properties shaped by both formation conditions and migration processes.
arXiv:2606.11724v1 Announce Type: new Abstract: Theory of Mind (ToM) reasoning requires inferring agents' beliefs from partial and asymmetric observations, which remains an open challenge for LLMs. Ex
arXiv:2606.12106v1 Announce Type: cross Abstract: This paper presents our solution to the 2026 SoccerNet VQA Challenge. We first develop a cost-effective data synthesis pipeline driven by a Vision-Lan
arXiv:2606.12240v1 Announce Type: cross Abstract: Multivariate time-series data often exhibit complex temporal dependencies, irregular sampling, and heterogeneous dynamics across multiple time scales,
arXiv:2606.11739v1 Announce Type: cross Abstract: We introduce a multi-view in-cabin monitoring dataset for public transportation with synchronized RGB and depth images from four inward-facing cameras
arXiv:2606.11363v1 Announce Type: new Abstract: Vector quantization is central to modern generative modeling pipelines, but large-codebook VQ models often suffer from codebook collapse. We identify en
arXiv:2507.21164v2 Announce Type: replace-cross Abstract: Unsupervised anomaly detection (UAD) aims to detect anomalies without labeled data, a necessity in many machine learning applications where an
arXiv:2606.12071v1 Announce Type: cross Abstract: LLMs are increasingly used to generate and judge scientific ideas. This makes novelty evaluation a central problem. Full idea evaluation is difficult